diff --git a/test/datasets/test_udpos.py b/test/datasets/test_udpos.py
new file mode 100644
index 0000000000..b66a6c681d
--- /dev/null
+++ b/test/datasets/test_udpos.py
@@ -0,0 +1,86 @@
+import os
+import random
+import string
+import zipfile
+from collections import defaultdict
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.datasets.udpos import UDPOS
+
+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, "UDPOS")
+    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 in ["train.txt", "dev.txt", "test.txt"]:
+        txt_file = os.path.join(temp_dataset_dir, file_name)
+        mocked_lines = mocked_data[os.path.splitext(file_name)[0]]
+        with open(txt_file, "w") as f:
+            for i in range(5):
+                rand_strings = ["".join(random.sample(string.ascii_letters, random.randint(1, 10))) for i in range(seed)]
+                rand_label_1 = [random.choice(string.ascii_letters) for i in range(seed)]
+                rand_label_2 = [random.choice(string.ascii_letters) for i in range(seed)]
+                # one token per line (each sample ends with an extra \n)
+                for rand_string, label_1, label_2 in zip(rand_strings, rand_label_1, rand_label_2):
+                    f.write(f"{rand_string}\t{label_1}\t{label_2}\n")
+                f.write("\n")
+                dataset_line = (rand_strings, rand_label_1, rand_label_2)
+                # append line to correct dataset split
+                mocked_lines.append(dataset_line)
+                seed += 1
+
+    # en-ud-v2.zip
+    compressed_dataset_path = os.path.join(base_dir, "en-ud-v2.zip")
+    # create zip file from dataset folder
+    with zipfile.ZipFile(compressed_dataset_path, "w") as zip_file:
+        for file_name in ("train.txt", "dev.txt", "test.txt"):
+            txt_file = os.path.join(temp_dataset_dir, file_name)
+            zip_file.write(txt_file, arcname=os.path.join("UDPOS", file_name))
+
+    return mocked_data
+
+
+class TestUDPOS(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", "valid", "test"])
+    def test_udpos(self, split):
+        dataset = UDPOS(root=self.root_dir, split=split)
+        samples = list(dataset)
+        expected_samples = self.samples[split] if split != "valid" else self.samples["dev"]
+        for sample, expected_sample in zip_equal(samples, expected_samples):
+            self.assertEqual(sample, expected_sample)
+
+    @parameterized.expand(["train", "valid", "test"])
+    def test_udpos_split_argument(self, split):
+        dataset1 = UDPOS(root=self.root_dir, split=split)
+        (dataset2,) = UDPOS(root=self.root_dir, split=(split,))
+
+        for d1, d2 in zip_equal(dataset1, dataset2):
+            self.assertEqual(d1, d2)