diff --git a/torchtext/datasets/dbpedia.py b/torchtext/datasets/dbpedia.py
index 54d97a9f73..b3a3c7677a 100644
--- a/torchtext/datasets/dbpedia.py
+++ b/torchtext/datasets/dbpedia.py
@@ -1,31 +1,32 @@
-from torchtext._internal.module_utils import is_module_available
 from typing import Union, Tuple
 
+from torchtext._internal.module_utils import is_module_available
+
 if is_module_available("torchdata"):
     from torchdata.datapipes.iter import FileOpener, GDriveReader, IterableWrapper
 
+import os
+
 from torchtext.data.datasets_utils import (
     _wrap_split_argument,
     _add_docstring_header,
     _create_dataset_directory,
 )
 
-import os
-
-URL = 'https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k'
+URL = "https://drive.google.com/uc?export=download&id=0Bz8a_Dbh9QhbQ2Vic1kxMmZZQ1k"
 
-MD5 = 'dca7b1ae12b1091090db52aa7ec5ca64'
+MD5 = "dca7b1ae12b1091090db52aa7ec5ca64"
 
 NUM_LINES = {
-    'train': 560000,
-    'test': 70000,
+    "train": 560000,
+    "test": 70000,
 }
 
-_PATH = 'dbpedia_csv.tar.gz'
+_PATH = "dbpedia_csv.tar.gz"
 
 _EXTRACTED_FILES = {
     "train": os.path.join("dbpedia_csv", "train.csv"),
-    "test": os.path.join("dbpedia_csv", "test.csv")
+    "test": os.path.join("dbpedia_csv", "test.csv"),
 }
 
 DATASET_NAME = "DBpedia"
@@ -37,19 +38,31 @@
 def DBpedia(root: str, split: Union[Tuple[str], str]):
     # TODO Remove this after removing conditional dependency
     if not is_module_available("torchdata"):
-        raise ModuleNotFoundError("Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`")
+        raise ModuleNotFoundError(
+            "Package `torchdata` not found. Please install following instructions at `https://github.com/pytorch/data`"
+        )
 
     url_dp = IterableWrapper([URL])
-
-    cache_dp = url_dp.on_disk_cache(
+    cache_compressed_dp = url_dp.on_disk_cache(
         filepath_fn=lambda x: os.path.join(root, _PATH),
-        hash_dict={os.path.join(root, _PATH): MD5}, hash_type="md5"
+        hash_dict={os.path.join(root, _PATH): MD5},
+        hash_type="md5",
+    )
+    cache_compressed_dp = GDriveReader(cache_compressed_dp).end_caching(
+        mode="wb", same_filepath_fn=True
     )
-    cache_dp = GDriveReader(cache_dp).end_caching(mode="wb", same_filepath_fn=True)
-    cache_dp = FileOpener(cache_dp, mode="b")
-
-    extracted_files = cache_dp.read_from_tar()
 
-    filter_extracted_files = extracted_files.filter(lambda x: _EXTRACTED_FILES[split] in x[0])
+    cache_decompressed_dp = cache_compressed_dp.on_disk_cache(
+        filepath_fn=lambda x: os.path.join(root, _EXTRACTED_FILES[split])
+    )
+    cache_decompressed_dp = (
+        FileOpener(cache_decompressed_dp, mode="b")
+        .read_from_tar()
+        .filter(lambda x: _EXTRACTED_FILES[split] in x[0])
+    )
+    cache_decompressed_dp = cache_decompressed_dp.end_caching(
+        mode="wb", same_filepath_fn=True
+    )
 
-    return filter_extracted_files.parse_csv().map(fn=lambda t: (int(t[0]), " ".join(t[1:])))
+    data_dp = FileOpener(cache_decompressed_dp, mode="b")
+    return data_dp.parse_csv().map(fn=lambda t: (int(t[0]), " ".join(t[1:])))