diff --git a/examples/training/train_textcat.py b/examples/training/train_textcat.py
index 73c8e9f2b..b072bd14e 100644
--- a/examples/training/train_textcat.py
+++ b/examples/training/train_textcat.py
@@ -44,6 +44,7 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
 
     # add label to text classifier
     textcat.add_label("POSITIVE")
+    textcat.add_label("NEGATIVE")
 
     # load the IMDB dataset
     print("Loading IMDB data...")
@@ -64,7 +65,7 @@ def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
         for i in range(n_iter):
             losses = {}
             # batch up the examples using spaCy's minibatch
-            batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
+            batches = minibatch(train_data, size=compounding(4.0, 16.0, 1.001))
             for batch in batches:
                 texts, annotations = zip(*batch)
                 nlp.update(texts, annotations, sgd=optimizer, drop=0.2, losses=losses)
@@ -106,22 +107,24 @@ def load_data(limit=0, split=0.8):
     random.shuffle(train_data)
     train_data = train_data[-limit:]
     texts, labels = zip(*train_data)
-    cats = [{"POSITIVE": bool(y)} for y in labels]
+    cats = [{"POSITIVE": bool(y), "NEGATIVE": not bool(y)} for y in labels]
     split = int(len(train_data) * split)
     return (texts[:split], cats[:split]), (texts[split:], cats[split:])
 
 
 def evaluate(tokenizer, textcat, texts, cats):
     docs = (tokenizer(text) for text in texts)
-    tp = 1e-8  # True positives
+    tp = 0.0  # True positives
     fp = 1e-8  # False positives
     fn = 1e-8  # False negatives
-    tn = 1e-8  # True negatives
+    tn = 0.0  # True negatives
     for i, doc in enumerate(textcat.pipe(docs)):
         gold = cats[i]
         for label, score in doc.cats.items():
             if label not in gold:
                 continue
+            if label == "NEGATIVE":
+                continue
             if score >= 0.5 and gold[label] >= 0.5:
                 tp += 1.0
             elif score >= 0.5 and gold[label] < 0.5:
diff --git a/spacy/_ml.py b/spacy/_ml.py
index c7c90398b..3a25aab1c 100644
--- a/spacy/_ml.py
+++ b/spacy/_ml.py
@@ -5,7 +5,7 @@ import numpy
 from thinc.v2v import Model, Maxout, Softmax, Affine, ReLu
 from thinc.i2v import HashEmbed, StaticVectors
 from thinc.t2t import ExtractWindow, ParametricAttention
-from thinc.t2v import Pooling, sum_pool
+from thinc.t2v import Pooling, sum_pool, mean_pool
 from thinc.misc import Residual
 from thinc.misc import LayerNorm as LN
 from thinc.misc import FeatureExtracter
@@ -575,6 +575,32 @@ def build_text_classifier(nr_class, width=64, **cfg):
     return model
 
 
+def build_simple_cnn_text_classifier(tok2vec, nr_class, exclusive_classes=True, **cfg):
+    """
+    Build a simple CNN text classifier, given a token-to-vector model as inputs.
+    If exclusive_classes=True, a softmax non-linearity is applied, so that the
+    outputs sum to 1. If exclusive_classes=False, a logistic non-linearity
+    is applied instead, so that outputs are in the range [0, 1].
+    """
+    with Model.define_operators({">>": chain}):
+        if exclusive_classes:
+            output_layer = Softmax(nr_class, tok2vec.nO)
+        else:
+            output_layer = (
+                zero_init(Affine(nr_class, tok2vec.nO))
+                >> logistic
+            )
+        model = (
+            tok2vec
+            >> flatten_add_lengths
+            >> Pooling(mean_pool)
+            >> output_layer
+        )
+    model.tok2vec = chain(tok2vec, flatten)
+    model.nO = nr_class
+    return model
+
+
 @layerize
 def flatten(seqs, drop=0.0):
     ops = Model.ops
diff --git a/spacy/pipeline.pyx b/spacy/pipeline.pyx
index b1e046b5b..0f59acad5 100644
--- a/spacy/pipeline.pyx
+++ b/spacy/pipeline.pyx
@@ -30,6 +30,7 @@ from .tokens.span import Span
 from .attrs import POS, ID
 from .parts_of_speech import X
 from ._ml import Tok2Vec, build_text_classifier, build_tagger_model
+from ._ml import build_simple_cnn_text_classifier
 from ._ml import link_vectors_to_models, zero_init, flatten
 from ._ml import create_default_optimizer
 from .errors import Errors, TempErrors
@@ -1043,15 +1044,20 @@ class TextCategorizer(Pipe):
 
     @classmethod
     def Model(cls, nr_class, **cfg):
-        return build_text_classifier(nr_class, **cfg)
+        embed_size = util.env_opt("embed_size", 2000)
+        if "token_vector_width" in cfg:
+            token_vector_width = cfg["token_vector_width"]
+        else:
+            token_vector_width = util.env_opt("token_vector_width", 96)
+        tok2vec = Tok2Vec(token_vector_width, embed_size, **cfg)
+        return build_simple_cnn_text_classifier(tok2vec, nr_class, **cfg)
 
     @property
     def tok2vec(self):
         if self.model in (None, True, False):
             return None
         else:
-            return chain(self.model.tok2vec, flatten)
-
+            return self.model.tok2vec
 
     def __init__(self, vocab, model=True, **cfg):
         self.vocab = vocab