diff --git a/examples/training/pretrain_textcat.py b/examples/training/pretrain_textcat.py
index c272145f5..8dc4e0786 100644
--- a/examples/training/pretrain_textcat.py
+++ b/examples/training/pretrain_textcat.py
@@ -47,8 +47,8 @@ def load_textcat_data(limit=0):
     train_data = train_data[-limit:]
     texts, labels = zip(*train_data)
     eval_texts, eval_labels = zip(*eval_data)
-    cats = [{'POSITIVE': bool(y)} for y in labels]
-    eval_cats = [{'POSITIVE': bool(y)} for y in eval_labels]
+    cats = [{'POSITIVE': bool(y), 'NEGATIVE': not bool(y)} for y in labels]
+    eval_cats = [{'POSITIVE': bool(y), 'NEGATIVE': not bool(y)} for y in eval_labels]
     return (texts, cats), (eval_texts, eval_cats)
 
 
@@ -61,9 +61,9 @@ def prefer_gpu():
 
 
 def build_textcat_model(tok2vec, nr_class, width):
-    from thinc.v2v import Model, Affine, Maxout
+    from thinc.v2v import Model, Softmax, Maxout
     from thinc.api import flatten_add_lengths, chain
-    from thinc.t2v import Pooling, sum_pool, max_pool
+    from thinc.t2v import Pooling, sum_pool, mean_pool, max_pool
     from thinc.misc import Residual, LayerNorm
     from spacy._ml import logistic, zero_init
 
@@ -71,11 +71,8 @@ def build_textcat_model(tok2vec, nr_class, width):
         model = (
             tok2vec
             >> flatten_add_lengths
-            >> Pooling(sum_pool, max_pool)
-            >> Residual(LayerNorm(Maxout(width*2, width*2, pieces=3)))
-            >> Residual(LayerNorm(Maxout(width*2, width*2, pieces=3)))
-            >> zero_init(Affine(nr_class, width*2, drop_factor=0.0))
-            >> logistic
+            >> Pooling(mean_pool)
+            >> Softmax(nr_class, width)
         )
     model.tok2vec = tok2vec
     return model
@@ -92,9 +89,9 @@ def create_pipeline(width, embed_size, vectors_model):
     nlp = spacy.load(vectors_model)
     print("Start training")
     textcat = TextCategorizer(nlp.vocab, 
-        labels=['POSITIVE'],
+        labels=['POSITIVE', 'NEGATIVE'],
         model=build_textcat_model(
-            Tok2Vec(width=width, embed_size=embed_size), 1, width))
+            Tok2Vec(width=width, embed_size=embed_size), 2, width))
 
     nlp.add_pipe(textcat)
     return nlp