diff --git a/spacy/tests/training/test_pretraining.py b/spacy/tests/training/test_pretraining.py
index 41cbddcae..f33089f61 100644
--- a/spacy/tests/training/test_pretraining.py
+++ b/spacy/tests/training/test_pretraining.py
@@ -265,50 +265,50 @@ def test_pretraining_tagger():
 
 
 # Try to debug segfault on windows
-# def test_pretraining_training():
-#    """Test that training can use a pretrained Tok2Vec model"""
-#    config = Config().from_str(pretrain_string_internal)
-#    nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
-#    filled = nlp.config
-#    pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
-#    filled = pretrain_config.merge(filled)
-#    train_config = util.load_config(DEFAULT_CONFIG_PATH)
-#    filled = train_config.merge(filled)
-#    with make_tempdir() as tmp_dir:
-#        pretrain_dir = tmp_dir / "pretrain"
-#        pretrain_dir.mkdir()
-#        file_path = write_sample_jsonl(pretrain_dir)
-#        filled["paths"]["raw_text"] = file_path
-#        filled["pretraining"]["component"] = "tagger"
-#        filled["pretraining"]["layer"] = "tok2vec"
-#        train_dir = tmp_dir / "train"
-#        train_dir.mkdir()
-#        train_path, dev_path = write_sample_training(train_dir)
-#        filled["paths"]["train"] = train_path
-#        filled["paths"]["dev"] = dev_path
-#        filled = filled.interpolate()
-#        P = filled["pretraining"]
-#        nlp_base = init_nlp(filled)
-#        model_base = (
-#            nlp_base.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
-#        )
-#        embed_base = None
-#        for node in model_base.walk():
-#            if node.name == "hashembed":
-#                embed_base = node
-#        pretrain(filled, pretrain_dir)
-#        pretrained_model = Path(pretrain_dir / "model3.bin")
-#        assert pretrained_model.exists()
-#        filled["initialize"]["init_tok2vec"] = str(pretrained_model)
-#        nlp = init_nlp(filled)
-#        model = nlp.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
-#        embed = None
-#        for node in model.walk():
-#            if node.name == "hashembed":
-#                embed = node
-#        # ensure that the tok2vec weights are actually changed by the pretraining
-#        assert np.any(np.not_equal(embed.get_param("E"), embed_base.get_param("E")))
-#        train(nlp, train_dir)
+def test_pretraining_training():
+    """Test that training can use a pretrained Tok2Vec model"""
+    config = Config().from_str(pretrain_string_internal)
+    nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
+    filled = nlp.config
+    pretrain_config = util.load_config(DEFAULT_CONFIG_PRETRAIN_PATH)
+    filled = pretrain_config.merge(filled)
+    train_config = util.load_config(DEFAULT_CONFIG_PATH)
+    filled = train_config.merge(filled)
+    with make_tempdir() as tmp_dir:
+        pretrain_dir = tmp_dir / "pretrain"
+        pretrain_dir.mkdir()
+        file_path = write_sample_jsonl(pretrain_dir)
+        filled["paths"]["raw_text"] = file_path
+        filled["pretraining"]["component"] = "tagger"
+        filled["pretraining"]["layer"] = "tok2vec"
+        train_dir = tmp_dir / "train"
+        train_dir.mkdir()
+        train_path, dev_path = write_sample_training(train_dir)
+        filled["paths"]["train"] = train_path
+        filled["paths"]["dev"] = dev_path
+        filled = filled.interpolate()
+        P = filled["pretraining"]
+        nlp_base = init_nlp(filled)
+        model_base = (
+            nlp_base.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
+        )
+        embed_base = None
+        for node in model_base.walk():
+            if node.name == "hashembed":
+                embed_base = node
+        pretrain(filled, pretrain_dir)
+        pretrained_model = Path(pretrain_dir / "model3.bin")
+        assert pretrained_model.exists()
+        filled["initialize"]["init_tok2vec"] = str(pretrained_model)
+        nlp = init_nlp(filled)
+        model = nlp.get_pipe(P["component"]).model.get_ref(P["layer"]).get_ref("embed")
+        embed = None
+        for node in model.walk():
+            if node.name == "hashembed":
+                embed = node
+        # ensure that the tok2vec weights are actually changed by the pretraining
+        assert np.any(np.not_equal(embed.get_param("E"), embed_base.get_param("E")))
+        train(nlp, train_dir)
 
 
 def write_sample_jsonl(tmp_dir):