diff --git a/examples/training/train_textcat.py b/examples/training/train_textcat.py
index cb65b8c8b..d94fbfd4a 100644
--- a/examples/training/train_textcat.py
+++ b/examples/training/train_textcat.py
@@ -20,6 +20,7 @@ import spacy
 from spacy import util
 from spacy.util import minibatch, compounding
 from spacy.gold import Example
+from thinc.api import Config
 
 
 @plac.annotations(
@@ -42,8 +43,9 @@ def main(config_path, output_dir=None, n_iter=20, n_texts=2000, init_tok2vec=Non
             output_dir.mkdir()
 
     print(f"Loading nlp model from {config_path}")
-    nlp_config = util.load_config(config_path, create_objects=False)["nlp"]
-    nlp = util.load_model_from_config(nlp_config)
+    nlp_config = Config().from_disk(config_path)
+    print(f"config: {nlp_config}")
+    nlp, _ = util.load_model_from_config(nlp_config)
 
     # ensure the nlp object was defined with a textcat component
     if "textcat" not in nlp.pipe_names:
diff --git a/examples/training/train_textcat_config.cfg b/examples/training/train_textcat_config.cfg
index 7c0f36b57..a1f4e91ce 100644
--- a/examples/training/train_textcat_config.cfg
+++ b/examples/training/train_textcat_config.cfg
@@ -1,19 +1,14 @@
 [nlp]
 lang = "en"
+pipeline = ["textcat"]
 
-[nlp.pipeline.textcat]
+[components]
+
+[components.textcat]
 factory = "textcat"
 
-[nlp.pipeline.textcat.model]
-@architectures = "spacy.TextCatCNN.v1"
-exclusive_classes = false
-
-[nlp.pipeline.textcat.model.tok2vec]
-@architectures = "spacy.HashEmbedCNN.v1"
-pretrained_vectors = null
-width = 96
-depth = 4
-embed_size = 2000
-window_size = 1
-maxout_pieces = 3
-subword_features = true
+[components.textcat.model]
+@architectures = "spacy.TextCatBOW.v1"
+exclusive_classes = true
+ngram_size = 1
+no_output_layer = false