diff --git a/requirements.txt b/requirements.txt
index 0440835f2..5bc1c8684 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,5 @@
 # Our libraries
-spacy-legacy>=3.0.10,<3.1.0
+spacy-legacy>=3.0.11,<3.1.0
 spacy-loggers>=1.0.0,<2.0.0
 cymem>=2.0.2,<2.1.0
 preshed>=3.0.2,<3.1.0
diff --git a/setup.cfg b/setup.cfg
index d290d706c..cee8c0c33 100644
--- a/setup.cfg
+++ b/setup.cfg
@@ -42,7 +42,7 @@ setup_requires =
     thinc>=8.1.0,<8.2.0
 install_requires =
     # Our libraries
-    spacy-legacy>=3.0.10,<3.1.0
+    spacy-legacy>=3.0.11,<3.1.0
     spacy-loggers>=1.0.0,<2.0.0
     murmurhash>=0.28.0,<1.1.0
     cymem>=2.0.2,<2.1.0
diff --git a/spacy/pipeline/textcat.py b/spacy/pipeline/textcat.py
index 65121114d..650a01949 100644
--- a/spacy/pipeline/textcat.py
+++ b/spacy/pipeline/textcat.py
@@ -74,7 +74,7 @@ subword_features = true
     default_config={
         "threshold": 0.0,
         "model": DEFAULT_SINGLE_TEXTCAT_MODEL,
-        "scorer": {"@scorers": "spacy.textcat_scorer.v1"},
+        "scorer": {"@scorers": "spacy.textcat_scorer.v2"},
     },
     default_score_weights={
         "cats_score": 1.0,
@@ -117,7 +117,7 @@ def textcat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
     )
 
 
-@registry.scorers("spacy.textcat_scorer.v1")
+@registry.scorers("spacy.textcat_scorer.v2")
 def make_textcat_scorer():
     return textcat_score
 
diff --git a/spacy/tests/pipeline/test_textcat.py b/spacy/tests/pipeline/test_textcat.py
index 155ce99a2..eafe4c128 100644
--- a/spacy/tests/pipeline/test_textcat.py
+++ b/spacy/tests/pipeline/test_textcat.py
@@ -895,3 +895,20 @@ def test_textcat_multi_threshold():
 
     scores = nlp.evaluate(train_examples, scorer_cfg={"threshold": 0})
     assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 1.0
+
+
+@pytest.mark.parametrize("component_name,scorer", [("textcat", "spacy.textcat_scorer.v1")])
+def test_textcat_legacy_scorers(component_name, scorer):
+    """Check that legacy scorers are registered and produce the expected score
+    keys."""
+    nlp = English()
+    nlp.add_pipe(component_name, config={"scorer": {"@scorers": scorer}})
+
+    train_examples = []
+    for text, annotations in TRAIN_DATA_SINGLE_LABEL:
+        train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
+    nlp.initialize(get_examples=lambda: train_examples)
+
+    # score the model (it's not actually trained but that doesn't matter)
+    scores = nlp.evaluate(train_examples)
+    assert 0 <= scores["cats_score"] <= 1