diff --git a/test/datasets/test_cc100.py b/test/datasets/test_cc100.py
new file mode 100644
index 0000000000..0188e7b44c
--- /dev/null
+++ b/test/datasets/test_cc100.py
@@ -0,0 +1,66 @@
+import os
+import random
+import string
+import lzma
+from parameterized import parameterized
+from collections import defaultdict
+from unittest.mock import patch
+
+from torchtext.datasets import CC100
+
+from ..common.case_utils import TempDirMixin, zip_equal
+from ..common.torchtext_test_case import TorchtextTestCase
+
+from torchtext.datasets.cc100 import VALID_CODES
+
+
+def _get_mock_dataset(root_dir):
+    """
+    root_dir: directory to the mocked dataset
+    """
+    base_dir = os.path.join(root_dir, "CC100")
+    os.makedirs(base_dir, exist_ok=True)
+
+    seed = 1
+    mocked_data = defaultdict(list)
+
+    for language_code in VALID_CODES:
+        file_name = f"{language_code}.txt.xz"
+        compressed_file = os.path.join(base_dir, file_name)
+        with lzma.open(compressed_file, "wt") as f:
+            for i in range(5):
+                rand_string = " ".join(
+                    random.choice(string.ascii_letters) for i in range(seed)
+                )
+                content = f"{rand_string}\n"
+                f.write(content)
+                mocked_data[language_code].append((language_code, rand_string))
+                seed += 1
+
+    return mocked_data
+
+
+class TestCC100(TempDirMixin, TorchtextTestCase):
+    @classmethod
+    def setUpClass(cls):
+        super().setUpClass()
+        cls.root_dir = cls.get_base_temp_dir()
+        cls.samples = _get_mock_dataset(cls.root_dir)
+        cls.patcher = patch(
+            "torchdata.datapipes.iter.util.cacheholder._hash_check", return_value=True
+        )
+        cls.patcher.start()
+
+    @classmethod
+    def tearDownClass(cls):
+        cls.patcher.stop()
+        super().tearDownClass()
+
+    @parameterized.expand(VALID_CODES)
+    def test_cc100(self, language_code):
+        dataset = CC100(root=self.root_dir, split="train", language_code=language_code)
+
+        samples = list(dataset)
+        expected_samples = self.samples[language_code]
+        for sample, expected_sample in zip_equal(samples, expected_samples):
+            self.assertEqual(sample, expected_sample)