diff --git a/examples/training/train_textcat.py b/examples/training/train_textcat.py
index 2f540b530..1f9cd29aa 100644
--- a/examples/training/train_textcat.py
+++ b/examples/training/train_textcat.py
@@ -2,7 +2,7 @@
 # coding: utf8
 """Train a multi-label convolutional neural network text classifier on the
 IMDB dataset, using the TextCategorizer component. The dataset will be loaded
-automatically via Thinc's built-in dataset loader. The model is then added to
+automatically via Thinc's built-in dataset loader. The model is added to
 spacy.pipeline, and predictions are available via `doc.cats`.
 
 For more details, see the documentation:
@@ -41,7 +41,7 @@ def main(model=None, output_dir=None, n_iter=20):
     if 'textcat' not in nlp.pipe_names:
         # textcat = nlp.create_pipe('textcat')
         textcat = TextCategorizer(nlp.vocab, labels=['POSITIVE'])
-        nlp.add_pipe(textcat, first=True)
+        nlp.add_pipe(textcat, last=True)
     # otherwise, get it, so we can add labels to it
     else:
         textcat = nlp.get_pipe('textcat')
diff --git a/website/usage/_training/_textcat.jade b/website/usage/_training/_textcat.jade
index 5c90519db..ad863bce1 100644
--- a/website/usage/_training/_textcat.jade
+++ b/website/usage/_training/_textcat.jade
@@ -1,13 +1,63 @@
 //- 💫 DOCS > USAGE > TRAINING > TEXT CLASSIFICATION
 
-+under-construction
-
-+h(3, "example-textcat") Example: Training spaCy's text classifier
++h(3, "example-textcat") Adding a text classifier to a spaCy model
     +tag-new(2)
 
 p
-    |  This example shows how to use and train spaCy's new
-    |  #[+api("textcategorizer") #[code TextCategorizer]] pipeline component
-    |  on IMDB movie reviews.
+    |  This example shows how to train a multi-label convolutional neural
+    |  network text classifier on IMDB movie reviews, using spaCy's new
+    |  #[+api("textcategorizer") #[code TextCategorizer]] component. The
+    |  dataset will be loaded automatically via Thinc's built-in dataset
+    |  loader. Predictions are available via
+    |  #[+api("doc#attributes") #[code Doc.cats]].
 
 +github("spacy", "examples/training/train_textcat.py")
+
++h(4) Step by step guide
+
++list("numbers")
+    +item
+        |  #[strong Load the model] you want to start with, or create an
+        |  #[strong empty model] using
+        |  #[+api("spacy#blank") #[code spacy.blank]] with the ID of your
+        |  language. If you're using a blank model, don't forget to add the
+        |  parser to the pipeline. If you're using an existing model,
+        |  make sure to disable all other pipeline components during training
+        |  using #[+api("language#disable_pipes") #[code nlp.disable_pipes]].
+        |  This way, you'll only be training the parser.
+
+    +item
+        |  #[strong Add the text classifier] to the pipeline, and add the labels
+        |  you want to train – for example, #[code POSITIVE].
+
+    +item
+        |  #[strong Load and pre-process the dataset], shuffle the data and
+        |  split off a part of it to hold back for evaluation. This way, you'll
+        |  be able to see results on each training iteration.
+
+    +item
+        |  #[strong Loop over] the training examples, partition them into
+        |  batches and create #[code Doc] and #[code GoldParse] objects for each
+        |  example in the batch.
+
+    +item
+        |  #[strong Update the model] by calling
+        |  #[+api("language#update") #[code nlp.update]], which steps
+        |  through the examples and makes a #[strong prediction]. It then
+        |  consults the annotations provided on the #[code GoldParse] instance,
+        |  to see whether it was right. If it was wrong, it adjusts its weights
+        |  so that the correct prediction will score higher next time.
+
+    +item
+        |  Optionally, you can also #[strong evaluate the text classifier] on
+        |  each iteration, by checking how it performs on the development data
+        |  held back from the dataset. This lets you print the
+        |  #[strong precision], #[strong recall] and #[strong F-score].
+
+    +item
+        |  #[strong Save] the trained model using
+        |  #[+api("language#to_disk") #[code nlp.to_disk]].
+
+    +item
+        |  #[strong Test] the model to make sure the text classifier works as
+        |  expected.
diff --git a/website/usage/examples.jade b/website/usage/examples.jade
index 808810364..525d584a1 100644
--- a/website/usage/examples.jade
+++ b/website/usage/examples.jade
@@ -113,9 +113,12 @@ include ../_includes/_mixins
         +tag-new(2)
 
     p
-        |  This example shows how to use and train spaCy's new
-        |  #[+api("textcategorizer") #[code TextCategorizer]] pipeline component
-        |  on IMDB movie reviews.
+        |  This example shows how to train a multi-label convolutional neural
+        |  network text classifier on IMDB movie reviews, using spaCy's new
+        |  #[+api("textcategorizer") #[code TextCategorizer]] component. The
+        |  dataset will be loaded automatically via Thinc's built-in dataset
+        |  loader. Predictions are available via
+        |  #[+api("doc#attributes") #[code Doc.cats]].
 
     +github("spacy", "examples/training/train_textcat.py")
 
diff --git a/website/usage/text-classification.jade b/website/usage/text-classification.jade
index 8a0e93450..9e43d185c 100644
--- a/website/usage/text-classification.jade
+++ b/website/usage/text-classification.jade
@@ -2,8 +2,4 @@
 
 include ../_includes/_mixins
 
-+under-construction
-
-+h(2, "example") Example
-
-+github("spacy", "examples/training/train_textcat.py")
+include _training/_textcat