make it clear that the span_finder_suggester is more general (not specific to span_finder)

spacy/pipeline/span_finder.py

@@ -1,7 +1,7 @@
-from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, cast
+from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple
 
-from thinc.api import Config, Model, Ops, Optimizer, get_current_ops, set_dropout_rate
-from thinc.types import Floats2d, Ints1d, Ragged
+from thinc.api import Config, Model, Optimizer, set_dropout_rate
+from thinc.types import Floats2d
 
 from spacy.language import Language
 from spacy.pipeline.trainable_pipe import TrainablePipe
@@ -11,7 +11,7 @@ from spacy.training import Example
 from spacy.errors import Errors
 
 from ..util import registry
-from .spancat import DEFAULT_SPANS_KEY, Suggester
+from .spancat import DEFAULT_SPANS_KEY
 
 span_finder_default_config = """
 [model]
@@ -310,34 +310,3 @@ class SpanFinder(TrainablePipe):
             self.model.initialize(X=docs, Y=Y)
         else:
             self.model.initialize()
-
-
-@registry.misc("spacy.span_finder_suggester.v1")
-def build_span_finder_suggester(spans_key: str) -> Suggester:
-    """Suggest every candidate predicted by the SpanFinder"""
-
-    def span_finder_suggester(
-        docs: Iterable[Doc], *, ops: Optional[Ops] = None
-    ) -> Ragged:
-        if ops is None:
-            ops = get_current_ops()
-        spans = []
-        lengths = []
-        for doc in docs:
-            length = 0
-            if doc.spans[spans_key]:
-                for span in doc.spans[spans_key]:
-                    spans.append([span.start, span.end])
-                    length += 1
-
-            lengths.append(length)
-
-        lengths_array = cast(Ints1d, ops.asarray(lengths, dtype="i"))
-        if len(spans) > 0:
-            output = Ragged(ops.asarray(spans, dtype="i"), lengths_array)
-        else:
-            output = Ragged(ops.xp.zeros((0, 0), dtype="i"), lengths_array)
-
-        return output
-
-    return span_finder_suggester

spacy/pipeline/spancat.py

@@ -4,7 +4,7 @@ from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union,
 
 import numpy
 from thinc.api import Config, Model, Ops, Optimizer, get_current_ops, set_dropout_rate
-from thinc.types import Floats2d, Ints2d, Ragged
+from thinc.types import Floats2d, Ints1d, Ints2d, Ragged
 
 from ..compat import Protocol, runtime_checkable
 from ..errors import Errors
@@ -111,6 +111,29 @@ def ngram_suggester(
     return output
 
 
+def preset_spans_suggester(
+    docs: Iterable[Doc], spans_key: str, *, ops: Optional[Ops] = None
+) -> Ragged:
+    if ops is None:
+        ops = get_current_ops()
+    spans = []
+    lengths = []
+    for doc in docs:
+        length = 0
+        if doc.spans[spans_key]:
+            for span in doc.spans[spans_key]:
+                spans.append([span.start, span.end])
+                length += 1
+
+        lengths.append(length)
+    lengths_array = cast(Ints1d, ops.asarray(lengths, dtype="i"))
+    if len(spans) > 0:
+        output = Ragged(ops.asarray(spans, dtype="i"), lengths_array)
+    else:
+        output = Ragged(ops.xp.zeros((0, 0), dtype="i"), lengths_array)
+    return output
+
+
 @registry.misc("spacy.ngram_suggester.v1")
 def build_ngram_suggester(sizes: List[int]) -> Suggester:
     """Suggest all spans of the given lengths. Spans are returned as a ragged
@@ -129,6 +152,14 @@ def build_ngram_range_suggester(min_size: int, max_size: int) -> Suggester:
     return build_ngram_suggester(sizes)
 
 
+@registry.misc("spacy.preset_spans_suggester.v1")
+def build_preset_spans_suggester(spans_key: str) -> Suggester:
+    """Suggest all spans that are already stored in doc.spans[spans_key].
+    This is useful when an upstream component is used to set the spans
+    on the Doc such as a SpanRuler or SpanFinder."""
+    return partial(preset_spans_suggester, spans_key=spans_key)
+
+
 @Language.factory(
     "spancat",
     assigns=["doc.spans"],

spacy/tests/pipeline/test_span_finder.py

@@ -199,7 +199,7 @@ def test_span_finder_suggester():
     docs = [nlp("This is an example."), nlp("This is the second example.")]
     docs[0].spans[SPANS_KEY] = [docs[0][3:4]]
     docs[1].spans[SPANS_KEY] = [docs[1][0:4], docs[1][3:5]]
-    suggester = registry.misc.get("spacy.span_finder_suggester.v1")(
+    suggester = registry.misc.get("spacy.preset_spans_suggester.v1")(
         spans_key=SPANS_KEY
     )
     candidates = suggester(docs)