diff --git a/test/common/case_utils.py b/test/common/case_utils.py
index f8803894b0..9a9340fbff 100644
--- a/test/common/case_utils.py
+++ b/test/common/case_utils.py
@@ -1,6 +1,7 @@
 import os.path
 import tempfile
 import unittest
+from itertools import zip_longest
 
 from torchtext._internal.module_utils import is_module_available
 
@@ -37,4 +38,18 @@ def get_temp_path(self, *paths):
 
 def skipIfNoModule(module, display_name=None):
     display_name = display_name or module
-    return unittest.skipIf(not is_module_available(module), f'"{display_name}" is not available')
+    return unittest.skipIf(
+        not is_module_available(module), f'"{display_name}" is not available'
+    )
+
+
+def zip_equal(*iterables):
+    """With the regular Python `zip` function, if one iterable is longer than the other,
+    the remainder portions are ignored.This is resolved in Python 3.10 where we can use
+    `strict=True` in the `zip` function
+    """
+    sentinel = object()
+    for combo in zip_longest(*iterables, fillvalue=sentinel):
+        if sentinel in combo:
+            raise ValueError("Iterables have different lengths")
+        yield combo
diff --git a/test/datasets/test_sst2.py b/test/datasets/test_sst2.py
new file mode 100644
index 0000000000..29fdb6fbed
--- /dev/null
+++ b/test/datasets/test_sst2.py
@@ -0,0 +1,92 @@
+import os
+import random
+import string
+import zipfile
+from collections import defaultdict
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.datasets.sst2 import SST2
+
+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, "SST2")
+    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)
+    for file_name, (col1_name, col2_name) in zip(
+        ("train.tsv", "test.tsv", "dev.tsv"),
+        ((("sentence", "label"), ("sentence", "label"), ("index", "sentence"))),
+    ):
+        txt_file = os.path.join(temp_dataset_dir, file_name)
+        with open(txt_file, "w") as f:
+            f.write(f"{col1_name}\t{col2_name}\n")
+            for i in range(5):
+                label = seed % 2
+                rand_string = " ".join(
+                    random.choice(string.ascii_letters) for i in range(seed)
+                )
+                if file_name == "test.tsv":
+                    dataset_line = (f"{rand_string} .",)
+                    f.write(f"{i}\t{rand_string} .\n")
+                else:
+                    dataset_line = (f"{rand_string} .", label)
+                    f.write(f"{rand_string} .\t{label}\n")
+
+                # append line to correct dataset split
+                mocked_data[os.path.splitext(file_name)[0]].append(dataset_line)
+                seed += 1
+
+    compressed_dataset_path = os.path.join(base_dir, "SST-2.zip")
+    # create zip file from dataset folder
+    with zipfile.ZipFile(compressed_dataset_path, "w") as zip_file:
+        for file_name in ("train.tsv", "test.tsv", "dev.tsv"):
+            txt_file = os.path.join(temp_dataset_dir, file_name)
+            zip_file.write(txt_file, arcname=os.path.join("SST-2", file_name))
+
+    return mocked_data
+
+
+class TestSST2(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", "test", "dev"])
+    def test_sst2(self, split):
+        dataset = SST2(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", "test", "dev"])
+    def test_sst2_split_argument(self, split):
+        dataset1 = SST2(root=self.root_dir, split=split)
+        (dataset2,) = SST2(root=self.root_dir, split=(split,))
+
+        for d1, d2 in zip_equal(dataset1, dataset2):
+            self.assertEqual(d1, d2)