diff --git a/python/fastText/test/test_script.py b/python/fastText/test/test_script.py
index 7d7838750..276e5c3b3 100644
--- a/python/fastText/test/test_script.py
+++ b/python/fastText/test/test_script.py
@@ -392,6 +392,72 @@ def test_subword_vector(self):
             self.assertTrue(np.isclose(vec3, vec4, atol=1e-5, rtol=0).all())
             self.assertTrue(np.isclose(vec4, vec1, atol=1e-5, rtol=0).all())
 
+    # TODO: Compare with .vec file
+    def test_get_words(self):
+        f = load_model(self.output + '.bin')
+        words1, freq1 = f.get_words(include_freq=True)
+        words2 = f.get_words(include_freq=False)
+        self.assertEqual(len(words1), len(words2))
+        self.assertEqual(len(words1), len(freq1))
+        f = load_model(self.output_sup + '.bin')
+        words1, freq1 = f.get_words(include_freq=True)
+        words2 = f.get_words(include_freq=False)
+        self.assertEqual(len(words1), len(words2))
+        self.assertEqual(len(words1), len(freq1))
+
+    # TODO: Compare with .vec file for unsup
+    def test_get_labels(self):
+        f = load_model(self.output + '.bin')
+        labels1, freq1 = f.get_labels(include_freq=True)
+        labels2 = f.get_labels(include_freq=False)
+        words2 = f.get_words(include_freq=False)
+        self.assertEqual(len(labels1), len(labels2))
+        self.assertEqual(len(labels1), len(freq1))
+        self.assertEqual(len(labels1), len(words2))
+        for w1, w2 in zip(labels2, words2):
+            self.assertEqual(w1, w2)
+        f = load_model(self.output_sup + '.bin')
+        labels1, freq1 = f.get_labels(include_freq=True)
+        labels2 = f.get_labels(include_freq=False)
+        self.assertEqual(len(labels1), len(labels2))
+        self.assertEqual(len(labels1), len(freq1))
+
+    def test_exercise_is_quant(self):
+        f = load_model(self.output + '.bin')
+        gotError = False
+        try:
+            f.quantize()
+        except ValueError:
+            gotError = True
+        self.assertTrue(gotError)
+        f = load_model(self.output_sup + '.bin')
+        self.assertTrue(not f.is_quantized())
+        f.quantize()
+        self.assertTrue(f.is_quantized())
+
+    def test_newline_predict_sentence(self):
+        f = load_model(self.output_sup + '.bin')
+        sentence = get_random_words(1, 1000, 2000)[0]
+        f.predict(sentence, k=5)
+        sentence += "\n"
+        gotError = False
+        try:
+            f.predict(sentence, k=5)
+        except ValueError:
+            gotError = True
+        self.assertTrue(gotError)
+
+        f = load_model(self.output + '.bin')
+        sentence = get_random_words(1, 1000, 2000)[0]
+        f.get_sentence_vector(sentence)
+        sentence += "\n"
+        gotError = False
+        try:
+            f.get_sentence_vector(sentence)
+        except ValueError:
+            gotError = True
+        self.assertTrue(gotError)
+
 
 class TestFastTextPyIntegration(TestFastTextPy):
     @classmethod
@@ -502,13 +568,6 @@ def check(
 
     return sup_test
 
-# TODO:
-# Exercise get_words with and without include_freq
-# Exercise get_labels with and without include_freq
-# Compare labels to words for unsup model
-# Test failure for quantizing unsupervised model
-# Test isQuant
-# Test failure for predict/sentence vector if text includes \n
 
 if __name__ == "__main__":
     sup_job_lr = [0.25, 0.5, 0.5, 0.1, 0.1, 0.1, 0.05, 0.05]