diff --git a/README.md b/README.md
index 45f01fb..2447c2f 100644
--- a/README.md
+++ b/README.md
@@ -1,9 +1,11 @@
 # GPTC
+
 General-purpose text classifier in Python
 
 GPTC provides both a CLI tool and a Python library.
 
 ## CLI Tool
+
 ### Classifying text
 
     python -m gptc <modelfile>
@@ -23,20 +25,28 @@ category:probability, ...}` to stdout.
     python -m gptc <raw model file> -c|--compile <compiled model file>
 
 ## Library
+
 ### `gptc.Classifier(model)`
+
 Create a `Classifier` object using the given *compiled* model (as a dict, not
 JSON).
+
 #### `Classifier.confidence(text)`
+
 Classify `text`. Returns a dict of the format `{category: probability,
 category:probability, ...}`
+
 #### `Classifier.classify(text)`
+
 Classify `text`. Returns the category into which the text is placed (as a
 string), or `None` when it cannot classify the text.
+
 ## `gptc.compile(raw_model)`
 Compile a raw model (as a list, not JSON) and return the compiled model (as a
 dict).
 
 ## Model format
+
 This section explains the raw model format, which is how you should create and
 edit models.
 
@@ -55,6 +65,7 @@ in any way these Python objects can be. However, it is recommended to store
 them in JSON format for compatibility with the command-line tool.
 
 ## Example model
+
 An example model, which is designed to distinguish between texts written by
 Mark Twain and those written by William Shakespeare, is available in `models`.
 The raw model is in `models/raw.json`; the compiled model is in