diff --git a/examples/training/train_textcat.py b/examples/training/train_textcat.py
index 852635075..6fa79e75b 100644
--- a/examples/training/train_textcat.py
+++ b/examples/training/train_textcat.py
@@ -26,8 +26,9 @@ from spacy.pipeline import TextCategorizer
 @plac.annotations(
     model=("Model name. Defaults to blank 'en' model.", "option", "m", str),
     output_dir=("Optional output directory", "option", "o", Path),
+    n_examples=("Number of texts to train from", "option", "N", int),
     n_iter=("Number of training iterations", "option", "n", int))
-def main(model=None, output_dir=None, n_iter=20):
+def main(model=None, output_dir=None, n_iter=20, n_texts=2000):
     if model is not None:
         nlp = spacy.load(model)  # load existing spaCy model
         print("Loaded model '%s'" % model)
@@ -50,7 +51,8 @@ def main(model=None, output_dir=None, n_iter=20):
 
     # load the IMBD dataset
     print("Loading IMDB data...")
-    (train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=2000)
+    print("Using %d training examples" % n_texts)
+    (train_texts, train_cats), (dev_texts, dev_cats) = load_data(limit=n_texts)
     train_docs = [nlp.tokenizer(text) for text in train_texts]
     train_gold = [GoldParse(doc, cats=cats) for doc, cats in
                   zip(train_docs, train_cats)]
@@ -65,14 +67,14 @@ def main(model=None, output_dir=None, n_iter=20):
         for i in range(n_iter):
             losses = {}
             # batch up the examples using spaCy's minibatch
-            batches = minibatch(train_data, size=compounding(4., 128., 1.001))
+            batches = minibatch(train_data, size=compounding(4., 32., 1.001))
             for batch in batches:
                 docs, golds = zip(*batch)
                 nlp.update(docs, golds, sgd=optimizer, drop=0.2, losses=losses)
             with textcat.model.use_params(optimizer.averages):
                 # evaluate on the dev data split off in load_data()
                 scores = evaluate(nlp.tokenizer, textcat, dev_texts, dev_cats)
-            print('{0:.3f}\t{0:.3f}\t{0:.3f}\t{0:.3f}'  # print a simple table
+            print('{0:.3f}\t{1:.3f}\t{2:.3f}\t{3:.3f}'  # print a simple table
                   .format(losses['textcat'], scores['textcat_p'],
                           scores['textcat_r'], scores['textcat_f']))