diff --git a/gptc/__init__.py b/gptc/__init__.py
index feb7b85..b970c05 100644
--- a/gptc/__init__.py
+++ b/gptc/__init__.py
@@ -5,7 +5,6 @@
 from gptc.compiler import compile as compile
 from gptc.classifier import Classifier as Classifier
 from gptc.pack import pack as pack
-from gptc.tokenizer import has_emoji as has_emoji
 from gptc.model import Model as Model, deserialize as deserialize
 from gptc.exceptions import (
     GPTCError as GPTCError,
diff --git a/gptc/classifier.py b/gptc/classifier.py
index e18bad5..de9e0a8 100755
--- a/gptc/classifier.py
+++ b/gptc/classifier.py
@@ -29,7 +29,6 @@ class Classifier:
         self.model = model
         model_ngrams = model.max_ngram_length
         self.max_ngram_length = min(max_ngram_length, model_ngrams)
-        self.has_emoji = gptc.tokenizer.has_emoji and model.has_emoji
 
     def confidence(self, text: str) -> Dict[str, float]:
         """Classify text with confidence.
@@ -49,9 +48,7 @@ class Classifier:
 
         model = self.model.weights
 
-        tokens = gptc.tokenizer.tokenize(
-            text, self.max_ngram_length, self.has_emoji
-        )
+        tokens = gptc.tokenizer.tokenize(text, self.max_ngram_length)
         numbered_probs: Dict[int, float] = {}
         for word in tokens:
             try:
diff --git a/gptc/model.py b/gptc/model.py
index a55ec32..014189b 100644
--- a/gptc/model.py
+++ b/gptc/model.py
@@ -12,14 +12,10 @@ class Model:
         weights: Dict[int, List[int]],
         names: List[str],
         max_ngram_length: int,
-        has_emoji: Union[None, bool] = None,
     ):
         self.weights = weights
         self.names = names
         self.max_ngram_length = max_ngram_length
-        self.has_emoji = (
-            gptc.tokenizer.has_emoji if has_emoji is None else has_emoji
-        )
 
     def serialize(self) -> bytes:
         out = b"GPTC model v4\n"
@@ -28,7 +24,16 @@ class Model:
                 {
                     "names": self.names,
                     "max_ngram_length": self.max_ngram_length,
-                    "has_emoji": self.has_emoji,
+                    "has_emoji": True,
+                    # Due to an oversight in development, version 3.0.0 still
+                    # had the code used to make emoji support optional, even
+                    # though the `emoji` library was made a hard dependency.
+                    # Part of this code checked whether or not the model
+                    # supports emoji; deserialization would not work in 3.0.0
+                    # if the model was compiled without this field. Emoji are
+                    # always supported with 3.0.0 and newer when GPTC has been
+                    # installed correctly, so this value should always be True.
+                    # Related: #11
                 }
             ).encode("utf-8")
             + b"\n"
@@ -57,14 +62,11 @@ def deserialize(encoded_model: bytes) -> Model:
     try:
         names = config["names"]
         max_ngram_length = config["max_ngram_length"]
-        has_emoji = config["has_emoji"]
     except KeyError:
         raise InvalidModelError()
 
     if not (
-        isinstance(names, list)
-        and isinstance(max_ngram_length, int)
-        and isinstance(has_emoji, bool)
+        isinstance(names, list) and isinstance(max_ngram_length, int)
     ) or not all([isinstance(name, str) for name in names]):
         raise InvalidModelError()
 
@@ -86,4 +88,4 @@ def deserialize(encoded_model: bytes) -> Model:
         for code in weight_codes
     }
 
-    return Model(weights, names, max_ngram_length, has_emoji)
+    return Model(weights, names, max_ngram_length)
diff --git a/gptc/tokenizer.py b/gptc/tokenizer.py
index a9405da..67499db 100644
--- a/gptc/tokenizer.py
+++ b/gptc/tokenizer.py
@@ -3,38 +3,26 @@
 from typing import List, Union
 import hashlib
 import base64
-
-try:
-    import emoji
-
-    has_emoji = True
-except ImportError:
-    has_emoji = False
+import emoji
 
 
-def tokenize(
-    text: str, max_ngram_length: int = 1, use_emoji: bool = True
-) -> List[int]:
-    """Convert a string to a list of lemmas."""
-    converted_text: Union[str, List[str]] = text.lower()
-
-    if has_emoji and use_emoji:
-        text = text.lower()
-        parts = []
-        highest_end = 0
-        for emoji_part in emoji.emoji_list(text):
-            parts += list(text[highest_end : emoji_part["match_start"]])
-            parts.append(emoji_part["emoji"])
-            highest_end = emoji_part["match_end"]
-        parts += list(text[highest_end:])
-        converted_text = [part for part in parts if part]
+def tokenize(text: str, max_ngram_length: int = 1) -> List[int]:
+    text = text.lower()
+    parts = []
+    highest_end = 0
+    for emoji_part in emoji.emoji_list(text):
+        parts += list(text[highest_end : emoji_part["match_start"]])
+        parts.append(emoji_part["emoji"])
+        highest_end = emoji_part["match_end"]
+    parts += list(text[highest_end:])
+    converted_text = [part for part in parts if part]
 
     tokens = [""]
 
     for char in converted_text:
         if char.isalpha() or char == "'":
             tokens[-1] += char
-        elif has_emoji and emoji.is_emoji(char):
+        elif emoji.is_emoji(char):
             tokens.append(char)
             tokens.append("")
         elif tokens[-1] != "":