diff --git a/gptc/__init__.py b/gptc/__init__.py
old mode 100755
new mode 100644
index 4f032d3..e960207
--- a/gptc/__init__.py
+++ b/gptc/__init__.py
@@ -1,69 +1 @@
-#!/usr/bin/env python3
-import sys
-import spacy
-
-nlp = spacy.load('en_core_web_sm')
-
-def listify(text):
-    return [string.lemma_.lower() for string in nlp(text) if string.lemma_[0] in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ']
-
-
-def compile(raw_model):
-    categories = {}
-
-    for portion in raw_model:
-        text = listify(portion['text'])
-        category = portion['category']
-        try:
-            categories[category] += text
-        except KeyError:
-            categories[category] = text
-
-    categories_by_count = {}
-    
-    for category, text in categories.items():
-        categories_by_count[category] = {}
-        for word in text:
-            try:
-                categories_by_count[category][word] += 1/len(categories[category])
-            except KeyError:
-                categories_by_count[category][word] = 1/len(categories[category])
-    word_weights = {}
-    for category, words in categories_by_count.items():
-        for word, value in words.items():
-            try:
-                word_weights[word][category] = value
-            except KeyError:
-                word_weights[word] = {category:value}
-
-    return word_weights
-
-
-class Classifier:
-    def __init__(self, model, supress_uncompiled_model_warning=False):
-        if type(model) == dict:
-            self.model = model
-        else:
-            self.model = compile(model)
-            if not supress_uncompiled_model_warning:
-                print('WARNING: model was not compiled', file=sys.stderr)
-                print('This makes everything slow, because compiling models takes far longer than using them.', file=sys.stderr)
-        self.warn = supress_uncompiled_model_warning
-
-    def classify(self, text):
-        model = self.model
-        text = listify(text)
-        probs = {}
-        for word in text:
-            try:
-                for category, value in model[word].items():
-                    try:
-                        probs[category] += value
-                    except KeyError:
-                        probs[category] = value
-            except KeyError:
-                pass
-        try:
-            return sorted(probs.items(), key=lambda x: x[1])[-1][0]
-        except IndexError:
-            return None
+from gptc.gptc import compile, Classifier
diff --git a/gptc/gptc.py b/gptc/gptc.py
new file mode 100755
index 0000000..901af11
--- /dev/null
+++ b/gptc/gptc.py
@@ -0,0 +1,113 @@
+'''Main module for GPTC.'''
+
+import sys
+import spacy
+
+nlp = spacy.load('en_core_web_sm')
+
+def _listify(text):
+    """Convert a string to a list of lemmas. Internal use only."""
+    return [string.lemma_.lower() for string in nlp(text) if string.lemma_[0] in 'abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ']
+
+
+def compile(raw_model):
+    """Compile a raw model.
+
+    Parameters
+    ----------
+    raw_model : list of dict
+        A raw GPTC model.
+
+    Returns
+    -------
+    dict
+        A compiled GPTC model.
+
+    """
+
+    categories = {}
+
+    for portion in raw_model:
+        text = _listify(portion['text'])
+        category = portion['category']
+        try:
+            categories[category] += text
+        except KeyError:
+            categories[category] = text
+
+    categories_by_count = {}
+    
+    for category, text in categories.items():
+        categories_by_count[category] = {}
+        for word in text:
+            try:
+                categories_by_count[category][word] += 1/len(categories[category])
+            except KeyError:
+                categories_by_count[category][word] = 1/len(categories[category])
+    word_weights = {}
+    for category, words in categories_by_count.items():
+        for word, value in words.items():
+            try:
+                word_weights[word][category] = value
+            except KeyError:
+                word_weights[word] = {category:value}
+
+    return word_weights
+
+
+class Classifier:
+    """A text classifier.
+
+    Parameters
+    ----------
+    model : dict or list
+        A compiled or raw GPTC model. Please don't use raw models here.
+
+    Attributes
+    ----------
+    model : dict
+        The model used. This is always a compiled model.
+
+    """
+
+    def __init__(self, model, supress_uncompiled_model_warning=False):
+        if type(model) == dict:
+            self.model = model
+        else:
+            self.model = compile(model)
+            if not supress_uncompiled_model_warning:
+                print('WARNING: model was not compiled', file=sys.stderr)
+                print('This makes everything slow, because compiling models takes far longer than using them.', file=sys.stderr)
+        self.warn = supress_uncompiled_model_warning
+
+    def classify(self, text):
+        """Classify text.
+
+        Parameters
+        ----------
+        text : str
+            The text to classify
+
+        Returns
+        -------
+        str or None
+            The most likely category, or None if no guess was made.
+
+        """
+
+        model = self.model
+        text = _listify(text)
+        probs = {}
+        for word in text:
+            try:
+                for category, value in model[word].items():
+                    try:
+                        probs[category] += value
+                    except KeyError:
+                        probs[category] = value
+            except KeyError:
+                pass
+        try:
+            return sorted(probs.items(), key=lambda x: x[1])[-1][0]
+        except IndexError:
+            return None