diff --git a/spacy/tests/training/test_pretraining.py b/spacy/tests/training/test_pretraining.py
index 5e5f94622..22364bb78 100644
--- a/spacy/tests/training/test_pretraining.py
+++ b/spacy/tests/training/test_pretraining.py
@@ -264,50 +264,51 @@ def test_pretraining_tagger():
             pretrain(filled, tmp_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)
+# 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 write_sample_jsonl(tmp_dir):