diff --git a/test/datasets/test_enwik9.py b/test/datasets/test_enwik9.py
new file mode 100644
index 0000000000..1b79b201e6
--- /dev/null
+++ b/test/datasets/test_enwik9.py
@@ -0,0 +1,83 @@
+import os
+import random
+import string
+import zipfile
+from collections import defaultdict
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.datasets.enwik9 import EnWik9
+
+from ..common.case_utils import TempDirMixin, zip_equal
+from ..common.torchtext_test_case import TorchtextTestCase
+
+
+def _get_mock_dataset(root_dir):
+    """
+    root_dir: directory to the mocked dataset
+    """
+    base_dir = os.path.join(root_dir, "EnWik9")
+    temp_dataset_dir = os.path.join(base_dir, "temp_dataset_dir")
+    os.makedirs(temp_dataset_dir, exist_ok=True)
+
+    seed = 1
+    mocked_data = defaultdict(list)
+    file_name = "enwik9"
+    txt_file = os.path.join(temp_dataset_dir, file_name)
+    mocked_lines = mocked_data["train"]
+    with open(txt_file, "w") as f:
+        for i in range(5):
+            rand_string = "<" + " ".join(
+                random.choice(string.ascii_letters) for i in range(seed)
+            ) + ">"
+            dataset_line = (f"'{rand_string}'")
+            f.write(f"'{rand_string}'\n")
+
+            # append line to correct dataset split
+            mocked_lines.append(dataset_line)
+            seed += 1
+
+    compressed_dataset_path = os.path.join(base_dir, "enwik9.zip")
+    # create zip file from dataset folder
+    with zipfile.ZipFile(compressed_dataset_path, "w") as zip_file:
+        txt_file = os.path.join(temp_dataset_dir, file_name)
+        zip_file.write(txt_file, arcname=file_name)
+
+    return mocked_data
+
+
+class TestEnWik9(TempDirMixin, TorchtextTestCase):
+    root_dir = None
+    samples = []
+
+    @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(["train"])
+    def test_enwik9(self, split):
+        dataset = EnWik9(root=self.root_dir, split=split)
+
+        samples = list(dataset)
+        expected_samples = self.samples[split]
+        for sample, expected_sample in zip_equal(samples, expected_samples):
+            self.assertEqual(sample, expected_sample)
+
+    @parameterized.expand(["train"])
+    def test_enwik9_split_argument(self, split):
+        dataset1 = EnWik9(root=self.root_dir, split=split)
+        (dataset2,) = EnWik9(root=self.root_dir, split=(split,))
+
+        for d1, d2 in zip_equal(dataset1, dataset2):
+            self.assertEqual(d1, d2)