diff --git a/spacy/tests/pipeline/test_tagger.py b/spacy/tests/pipeline/test_tagger.py
index b1b52b9fa..a1aa7e1e1 100644
--- a/spacy/tests/pipeline/test_tagger.py
+++ b/spacy/tests/pipeline/test_tagger.py
@@ -28,8 +28,6 @@ def test_tagger_begin_training_tag_map():
 
 TAGS = ("N", "V", "J")
 
-MORPH_RULES = {"V": {"like": {"lemma": "luck"}}}
-
 TRAIN_DATA = [
     ("I like green eggs", {"tags": ["N", "V", "J", "N"]}),
     ("Eat blue ham", {"tags": ["V", "J", "N"]}),
diff --git a/spacy/tests/pipeline/test_textcat.py b/spacy/tests/pipeline/test_textcat.py
index 66c27b233..12ead90cb 100644
--- a/spacy/tests/pipeline/test_textcat.py
+++ b/spacy/tests/pipeline/test_textcat.py
@@ -84,9 +84,8 @@ def test_overfitting_IO():
     # Simple test to try and quickly overfit the textcat component - ensuring the ML models work correctly
     fix_random_seed(0)
     nlp = English()
-    textcat = nlp.add_pipe("textcat")
     # Set exclusive labels
-    textcat.model.attrs["multi_label"] = False
+    textcat = nlp.add_pipe("textcat", config={"model": {"exclusive_classes": True}})
     train_examples = []
     for text, annotations in TRAIN_DATA:
         train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
@@ -103,9 +102,8 @@ def test_overfitting_IO():
     test_text = "I am happy."
     doc = nlp(test_text)
     cats = doc.cats
-    # note that by default, exclusive_classes = false so we need a bigger error margin
-    assert cats["POSITIVE"] > 0.8
-    assert cats["POSITIVE"] + cats["NEGATIVE"] == pytest.approx(1.0, 0.1)
+    assert cats["POSITIVE"] > 0.9
+    assert cats["POSITIVE"] + cats["NEGATIVE"] == pytest.approx(1.0, 0.001)
 
     # Also test the results are still the same after IO
     with make_tempdir() as tmp_dir:
@@ -113,8 +111,8 @@ def test_overfitting_IO():
         nlp2 = util.load_model_from_path(tmp_dir)
         doc2 = nlp2(test_text)
         cats2 = doc2.cats
-        assert cats2["POSITIVE"] > 0.8
-        assert cats2["POSITIVE"] + cats2["NEGATIVE"] == pytest.approx(1.0, 0.1)
+        assert cats2["POSITIVE"] > 0.9
+        assert cats2["POSITIVE"] + cats2["NEGATIVE"] == pytest.approx(1.0, 0.001)
 
     # Test scoring
     scores = nlp.evaluate(train_examples, scorer_cfg={"positive_label": "POSITIVE"})