From 337a360cc7871f768988d60782d83b420fe24270 Mon Sep 17 00:00:00 2001
From: Adriane Boyd <adrianeboyd@gmail.com>
Date: Tue, 27 Jun 2023 19:32:17 +0200
Subject: [PATCH] Use spans_ prefix for default span finder scores (#12753)

---
 spacy/pipeline/span_finder.py            | 8 ++++----
 spacy/tests/pipeline/test_span_finder.py | 6 +++---
 2 files changed, 7 insertions(+), 7 deletions(-)

diff --git a/spacy/pipeline/span_finder.py b/spacy/pipeline/span_finder.py
index 91be2f2ae..53f5c55be 100644
--- a/spacy/pipeline/span_finder.py
+++ b/spacy/pipeline/span_finder.py
@@ -53,9 +53,9 @@ DEFAULT_SPAN_FINDER_MODEL = Config().from_str(span_finder_default_config)["model
         "scorer": {"@scorers": "spacy.span_finder_scorer.v1"},
     },
     default_score_weights={
-        f"span_finder_{DEFAULT_SPANS_KEY}_f": 1.0,
-        f"span_finder_{DEFAULT_SPANS_KEY}_p": 0.0,
-        f"span_finder_{DEFAULT_SPANS_KEY}_r": 0.0,
+        f"spans_{DEFAULT_SPANS_KEY}_f": 1.0,
+        f"spans_{DEFAULT_SPANS_KEY}_p": 0.0,
+        f"spans_{DEFAULT_SPANS_KEY}_r": 0.0,
     },
 )
 def make_span_finder(
@@ -104,7 +104,7 @@ def make_span_finder_scorer():
 
 def span_finder_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
     kwargs = dict(kwargs)
-    attr_prefix = "span_finder_"
+    attr_prefix = "spans_"
     key = kwargs["spans_key"]
     kwargs.setdefault("attr", f"{attr_prefix}{key}")
     kwargs.setdefault(
diff --git a/spacy/tests/pipeline/test_span_finder.py b/spacy/tests/pipeline/test_span_finder.py
index 1a8789fff..47a8a34a8 100644
--- a/spacy/tests/pipeline/test_span_finder.py
+++ b/spacy/tests/pipeline/test_span_finder.py
@@ -230,10 +230,10 @@ def test_overfitting_IO():
 
     # Test scoring
     scores = nlp.evaluate(train_examples)
-    assert f"span_finder_{SPANS_KEY}_f" in scores
+    assert f"spans_{SPANS_KEY}_f" in scores
     # It's not perfect 1.0 F1 because it's designed to overgenerate for now.
-    assert scores[f"span_finder_{SPANS_KEY}_p"] == 0.75
-    assert scores[f"span_finder_{SPANS_KEY}_r"] == 1.0
+    assert scores[f"spans_{SPANS_KEY}_p"] == 0.75
+    assert scores[f"spans_{SPANS_KEY}_r"] == 1.0
 
     # also test that the spancat works for just a single entity in a sentence
     doc = nlp("London")