diff --git a/spacy/scorer.py b/spacy/scorer.py
index da22d59d4..c50de3d43 100644
--- a/spacy/scorer.py
+++ b/spacy/scorer.py
@@ -240,7 +240,7 @@ class Scorer:
                             pred_per_feat[field].add((gold_i, feat))
             for field in per_feat:
                 per_feat[field].score_set(
-                    pred_per_feat.get(field, set()), gold_per_feat.get(field, set()),
+                    pred_per_feat.get(field, set()), gold_per_feat.get(field, set())
                 )
         result = {k: v.to_dict() for k, v in per_feat.items()}
         return {f"{attr}_per_feat": result}
@@ -418,9 +418,9 @@ class Scorer:
                     f_per_type[pred_label].fp += 1
         micro_prf = PRFScore()
         for label_prf in f_per_type.values():
-            micro_prf.tp = label_prf.tp
-            micro_prf.fn = label_prf.fn
-            micro_prf.fp = label_prf.fp
+            micro_prf.tp += label_prf.tp
+            micro_prf.fn += label_prf.fn
+            micro_prf.fp += label_prf.fp
         n_cats = len(f_per_type) + 1e-100
         macro_p = sum(prf.precision for prf in f_per_type.values()) / n_cats
         macro_r = sum(prf.recall for prf in f_per_type.values()) / n_cats
diff --git a/spacy/tests/pipeline/test_textcat.py b/spacy/tests/pipeline/test_textcat.py
index 99b5132ca..232b53e1d 100644
--- a/spacy/tests/pipeline/test_textcat.py
+++ b/spacy/tests/pipeline/test_textcat.py
@@ -8,6 +8,7 @@ from spacy.language import Language
 from spacy.pipeline import TextCategorizer
 from spacy.tokens import Doc
 from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
+from spacy.scorer import Scorer
 
 from ..util import make_tempdir
 from ...cli.train import verify_textcat_config
@@ -224,3 +225,31 @@ def test_positive_class_not_binary():
     assert textcat.labels == ("SOME", "THING", "POS")
     with pytest.raises(ValueError):
         verify_textcat_config(nlp, pipe_config)
+
+def test_textcat_evaluation():
+    train_examples = []
+    nlp = English()
+    ref1 = nlp("one")
+    ref1.cats = {"winter": 1.0, "summer": 1.0, "spring": 1.0, "autumn": 1.0}
+    pred1 = nlp("one")
+    pred1.cats = {"winter": 1.0, "summer": 0.0, "spring": 1.0, "autumn": 1.0}
+    train_examples.append(Example(pred1, ref1))
+
+    ref2 = nlp("two")
+    ref2.cats = {"winter": 0.0, "summer": 0.0, "spring": 1.0, "autumn": 1.0}
+    pred2 = nlp("two")
+    pred2.cats = {"winter": 1.0, "summer": 0.0, "spring": 0.0, "autumn": 1.0}
+    train_examples.append(Example(pred2, ref2))
+
+    scores = Scorer().score_cats(train_examples, "cats", labels=["winter", "summer", "spring", "autumn"])
+    assert scores["cats_f_per_type"]["winter"]["p"] == 1/2
+    assert scores["cats_f_per_type"]["winter"]["r"] == 1/1
+    assert scores["cats_f_per_type"]["summer"]["p"] == 0
+    assert scores["cats_f_per_type"]["summer"]["r"] == 0/1
+    assert scores["cats_f_per_type"]["spring"]["p"] == 1/1
+    assert scores["cats_f_per_type"]["spring"]["r"] == 1/2
+    assert scores["cats_f_per_type"]["autumn"]["p"] == 2/2
+    assert scores["cats_f_per_type"]["autumn"]["r"] == 2/2
+
+    assert scores["cats_micro_p"] == 4/5
+    assert scores["cats_micro_r"] == 4/6