Use simple suggester for spancat initialization (#10143)

Instead of the running the actual suggester, which may require
annotation from annotating components that is not necessarily present in
the reference docs, use the built-in 1-gram suggester.

spacy/pipeline/spancat.py

@@ -412,7 +412,7 @@ class SpanCategorizer(TrainablePipe):
         self._require_labels()
         if subbatch:
             docs = [eg.x for eg in subbatch]
-            spans = self.suggester(docs)
+            spans = build_ngram_suggester(sizes=[1])(docs)
             Y = self.model.ops.alloc2f(spans.dataXd.shape[0], len(self.labels))
             self.model.initialize(X=(docs, spans), Y=Y)
         else: