diff --git a/test/asset/SST-2.zip b/test/asset/SST2/SST-2.zip
similarity index 100%
rename from test/asset/SST-2.zip
rename to test/asset/SST2/SST-2.zip
diff --git a/test/experimental/test_datasets.py b/test/experimental/test_datasets.py
index 50af68ac4b..18807c4958 100644
--- a/test/experimental/test_datasets.py
+++ b/test/experimental/test_datasets.py
@@ -1,12 +1,9 @@
 import hashlib
 import json
-import os
-import shutil
-import tempfile
 
 from torchtext.experimental.datasets import sst2
 
-from ..common.assets import get_asset_path
+from ..common.assets import _ASSET_DIR
 from ..common.case_utils import skipIfNoModule
 from ..common.torchtext_test_case import TorchtextTestCase
 
@@ -14,42 +11,32 @@
 class TestDataset(TorchtextTestCase):
     @skipIfNoModule("torchdata")
     def test_sst2__dataset(self):
-        # copy the asset file into the expected download location
-        # note that this is just a zip file with the first 10 lines of the SST2 dataset
-        # test if providing a custom hash works with the dummy dataset
-        with tempfile.TemporaryDirectory() as dir_name:
-            asset_path = get_asset_path(sst2._PATH)
-            data_path = os.path.join(dir_name, sst2.DATASET_NAME, sst2._PATH)
-            os.makedirs(os.path.join(dir_name, sst2.DATASET_NAME))
-            shutil.copy(asset_path, data_path)
 
-            split = ("train", "dev", "test")
-            train_dataset, dev_dataset, test_dataset = sst2.SST2(
-                split=split, root=dir_name, validate_hash=False
-            )
+        split = ("train", "dev", "test")
+        train_dataset, dev_dataset, test_dataset = sst2.SST2(
+            split=split, root=_ASSET_DIR, validate_hash=False
+        )
 
-            # verify datasets objects are instances of SST2Dataset
-            for dataset in (train_dataset, dev_dataset, test_dataset):
-                self.assertTrue(isinstance(dataset, sst2.SST2Dataset))
+        # verify datasets objects are instances of SST2Dataset
+        for dataset in (train_dataset, dev_dataset, test_dataset):
+            self.assertTrue(isinstance(dataset, sst2.SST2Dataset))
 
-            # verify hashes of first line in dataset
-            self.assertEqual(
-                hashlib.md5(
-                    json.dumps(next(iter(train_dataset)), sort_keys=True).encode(
-                        "utf-8"
-                    )
-                ).hexdigest(),
-                sst2._FIRST_LINE_MD5["train"],
-            )
-            self.assertEqual(
-                hashlib.md5(
-                    json.dumps(next(iter(dev_dataset)), sort_keys=True).encode("utf-8")
-                ).hexdigest(),
-                sst2._FIRST_LINE_MD5["dev"],
-            )
-            self.assertEqual(
-                hashlib.md5(
-                    json.dumps(next(iter(test_dataset)), sort_keys=True).encode("utf-8")
-                ).hexdigest(),
-                sst2._FIRST_LINE_MD5["test"],
-            )
+        # verify hashes of first line in dataset
+        self.assertEqual(
+            hashlib.md5(
+                json.dumps(next(iter(train_dataset)), sort_keys=True).encode("utf-8")
+            ).hexdigest(),
+            sst2._FIRST_LINE_MD5["train"],
+        )
+        self.assertEqual(
+            hashlib.md5(
+                json.dumps(next(iter(dev_dataset)), sort_keys=True).encode("utf-8")
+            ).hexdigest(),
+            sst2._FIRST_LINE_MD5["dev"],
+        )
+        self.assertEqual(
+            hashlib.md5(
+                json.dumps(next(iter(test_dataset)), sort_keys=True).encode("utf-8")
+            ).hexdigest(),
+            sst2._FIRST_LINE_MD5["test"],
+        )
diff --git a/torchtext/experimental/datasets/sst2.py b/torchtext/experimental/datasets/sst2.py
index 95bac217f7..77d3134ac6 100644
--- a/torchtext/experimental/datasets/sst2.py
+++ b/torchtext/experimental/datasets/sst2.py
@@ -92,7 +92,9 @@ def _get_datapipe(self, root, split, validate_hash):
             )
 
         # extract data from zip
-        extracted_files = check_cache_dp.read_from_zip().filter(lambda x: split in x[0])
+        extracted_files = check_cache_dp.read_from_zip().filter(
+            lambda x: f"{split}.tsv" in x[0]
+        )
 
         # Parse CSV file and yield data samples
         return extracted_files.parse_csv(skip_lines=1, delimiter="\t").map(