Migrate SST2 from experimental to datasets folder

docs/source/datasets.rst

@@ -13,7 +13,7 @@ General use cases are as follows: ::
 
     def tokenize(label, line):
         return line.split()
-     
+
     tokens = []
     for label, line in train_iter:
         tokens += tokenize(label, line)
@@ -73,6 +73,11 @@ IMDb
 
 .. autofunction:: IMDB
 
+SST2
+~~~~
+
+.. autofunction:: SST2
+
 
 Language Modeling
 ^^^^^^^^^^^^^^^^^
@@ -152,4 +157,3 @@ EnWik9
 ~~~~~~
 
 .. autofunction:: EnWik9
-

torchtext/datasets/__init__.py

@@ -1,4 +1,5 @@
 import importlib
+
 from .ag_news import AG_NEWS
 from .amazonreviewfull import AmazonReviewFull
 from .amazonreviewpolarity import AmazonReviewPolarity
@@ -8,39 +9,41 @@
 from .imdb import IMDB
 from .iwslt2016 import IWSLT2016
 from .iwslt2017 import IWSLT2017
+from .multi30k import Multi30k
 from .penntreebank import PennTreebank
 from .sogounews import SogouNews
 from .squad1 import SQuAD1
 from .squad2 import SQuAD2
+from .sst2 import SST2
 from .udpos import UDPOS
 from .wikitext103 import WikiText103
 from .wikitext2 import WikiText2
 from .yahooanswers import YahooAnswers
 from .yelpreviewfull import YelpReviewFull
 from .yelpreviewpolarity import YelpReviewPolarity
-from .multi30k import Multi30k
 
 DATASETS = {
-    'AG_NEWS': AG_NEWS,
-    'AmazonReviewFull': AmazonReviewFull,
-    'AmazonReviewPolarity': AmazonReviewPolarity,
-    'CoNLL2000Chunking': CoNLL2000Chunking,
-    'DBpedia': DBpedia,
-    'EnWik9': EnWik9,
-    'IMDB': IMDB,
-    'IWSLT2016': IWSLT2016,
-    'IWSLT2017': IWSLT2017,
-    'PennTreebank': PennTreebank,
-    'SQuAD1': SQuAD1,
-    'SQuAD2': SQuAD2,
-    'SogouNews': SogouNews,
-    'UDPOS': UDPOS,
-    'WikiText103': WikiText103,
-    'WikiText2': WikiText2,
-    'YahooAnswers': YahooAnswers,
-    'YelpReviewFull': YelpReviewFull,
-    'YelpReviewPolarity': YelpReviewPolarity,
-    'Multi30k': Multi30k
+    "AG_NEWS": AG_NEWS,
+    "AmazonReviewFull": AmazonReviewFull,
+    "AmazonReviewPolarity": AmazonReviewPolarity,
+    "CoNLL2000Chunking": CoNLL2000Chunking,
+    "DBpedia": DBpedia,
+    "EnWik9": EnWik9,
+    "IMDB": IMDB,
+    "IWSLT2016": IWSLT2016,
+    "IWSLT2017": IWSLT2017,
+    "Multi30k": Multi30k,
+    "PennTreebank": PennTreebank,
+    "SQuAD1": SQuAD1,
+    "SQuAD2": SQuAD2,
+    "SogouNews": SogouNews,
+    "SST2": SST2,
+    "UDPOS": UDPOS,
+    "WikiText103": WikiText103,
+    "WikiText2": WikiText2,
+    "YahooAnswers": YahooAnswers,
+    "YelpReviewFull": YelpReviewFull,
+    "YelpReviewPolarity": YelpReviewPolarity,
 }
 
 URLS = {}

torchtext/datasets/sst2.py

@@ -0,0 +1,82 @@
+# Copyright (c) Facebook, Inc. and its affiliates.
+import os
+
+from torchtext._internal.module_utils import is_module_available
+from torchtext.data.datasets_utils import (
+    _add_docstring_header,
+    _create_dataset_directory,
+    _wrap_split_argument,
+)
+
+if is_module_available("torchdata"):
+    from torchdata.datapipes.iter import IterableWrapper, FileOpener
+
+    # we import HttpReader from _download_hooks so we can swap out public URLs
+    # with interal URLs when the dataset is used within Facebook
+    from torchtext._download_hooks import HttpReader
+
+
+URL = "https://dl.fbaipublicfiles.com/glue/data/SST-2.zip"
+
+MD5 = "9f81648d4199384278b86e315dac217c"
+
+NUM_LINES = {
+    "train": 67349,
+    "dev": 872,
+    "test": 1821,
+}
+
+_PATH = "SST-2.zip"
+
+DATASET_NAME = "SST2"
+
+_EXTRACTED_FILES = {
+    "train": os.path.join("SST-2", "train.tsv"),
+    "dev": os.path.join("SST-2", "dev.tsv"),
+    "test": os.path.join("SST-2", "test.tsv"),
+}
+
+
+@_add_docstring_header(num_lines=NUM_LINES, num_classes=2)
+@_create_dataset_directory(dataset_name=DATASET_NAME)
+@_wrap_split_argument(("train", "dev", "test"))
+def SST2(root, split):
+    # 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`"
+        )
+
+    url_dp = IterableWrapper([URL])
+    cache_compressed_dp = url_dp.on_disk_cache(
+        filepath_fn=lambda x: os.path.join(root, os.path.basename(x)),
+        hash_dict={os.path.join(root, os.path.basename(URL)): MD5},
+        hash_type="md5",
+    )
+    cache_compressed_dp = HttpReader(cache_compressed_dp).end_caching(
+        mode="wb", same_filepath_fn=True
+    )
+
+    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_zip()
+        .filter(lambda x: _EXTRACTED_FILES[split] in x[0])
+    )
+    cache_decompressed_dp = cache_decompressed_dp.end_caching(
+        mode="wb", same_filepath_fn=True
+    )
+
+    data_dp = FileOpener(cache_decompressed_dp, mode="b")
+    # test split for SST2 doesn't have labels
+    if split == "test":
+        parsed_data = data_dp.parse_csv(skip_lines=1, delimiter="\t").map(
+            lambda t: (t[1].strip(),)
+        )
+    else:
+        parsed_data = data_dp.parse_csv(skip_lines=1, delimiter="\t").map(
+            lambda t: (t[0].strip(), int(t[1]))
+        )
+    return parsed_data

torchtext/experimental/datasets/__init__.py

@@ -1,4 +1,3 @@
 from . import raw
-from . import sst2
 
-__all__ = ["raw", "sst2"]
+__all__ = ["raw"]