Handle case with nothing to score in span predictor

This case was not handled correctly. It may be desirable to make changes
in the coref component to make sure this doesn't happen, but the span
predictor should also handle this kind of data intelligently internally.

Note that something is still weird because the span predictor seems to
not be learning.

spacy/scorer.py

@@ -916,6 +916,10 @@ class Scorer:
             ref = eg.reference
             pred = eg.predicted
             for key, gold_sg in ref.spans.items():
+                #TODO it might be better to do something like pred.spans.get(key, [])
+                if len(gold_sg) == 0:
+                    # if there are no spans there's nothing to predict
+                    continue
                 if key.startswith(output_prefix):
                     pred_sg = pred.spans[key]
                     for gold_mention, pred_mention in zip(gold_sg, pred_sg):
@@ -924,6 +928,9 @@ class Scorer:
                         pred_starts.append(pred_mention.start)
                         pred_ends.append(pred_mention.end)
 
+            # it's possible there are no heads to predict from, in which case, skip
+            if len(starts) == 0:
+                continue
 
             # see how many are perfect
             cs = [a == b for a, b in zip(starts, pred_starts)]
@@ -933,7 +940,13 @@ class Scorer:
 
             scores.append(float(accuracy))
         out_key = f"span_{output_prefix}_accuracy"
-        return {out_key: mean(scores)}
+
+        # it is possible there was nothing to score
+        final = 0.0
+        if len(scores) > 0:
+            final = mean(scores)
+
+        return {out_key: final}
 
 
 def get_ner_prf(examples: Iterable[Example], **kwargs) -> Dict[str, Any]: