Add support for STS-B dataset with unit tests

docs/source/datasets.rst

@@ -62,6 +62,11 @@ SST2
 
 .. autofunction:: SST2
 
+STSB
+~~~~
+
+.. autofunction:: STSB
+
 YahooAnswers
 ~~~~~~~~~~~~
 

test/datasets/test_stsb.py

@@ -0,0 +1,89 @@
+import os
+import random
+import tarfile
+from collections import defaultdict
+from unittest.mock import patch
+
+from parameterized import parameterized
+from torchtext.datasets.stsb import STSB
+
+from ..common.case_utils import TempDirMixin, zip_equal, get_random_unicode
+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, "STSB")
+    temp_dataset_dir = os.path.join(base_dir, "stsbenchmark")
+    os.makedirs(temp_dataset_dir, exist_ok=True)
+
+    seed = 1
+    mocked_data = defaultdict(list)
+    for file_name, name in zip(["sts-train.csv", "sts-dev.csv" "sts-test.csv"], ["train", "dev", "test"]):
+        txt_file = os.path.join(temp_dataset_dir, file_name)
+        with open(txt_file, "w", encoding="utf-8") as f:
+            for i in range(5):
+                label = random.uniform(0, 5)
+                rand_string_1 = get_random_unicode(seed)
+                rand_string_2 = get_random_unicode(seed + 1)
+                rand_string_3 = get_random_unicode(seed + 2)
+                rand_string_4 = get_random_unicode(seed + 3)
+                rand_string_5 = get_random_unicode(seed + 4)
+                dataset_line = (i, label, rand_string_4, rand_string_5)
+                # append line to correct dataset split
+                mocked_data[name].append(dataset_line)
+                f.write(
+                    f"{rand_string_1}\t{rand_string_2}\t{rand_string_3}\t{i}\t{label}\t{rand_string_4}\t{rand_string_5}\n"
+                )
+                seed += 1
+            # case with quotes to test arg `quoting=csv.QUOTE_NONE`
+            dataset_line = (i, label, rand_string_4, rand_string_5)
+            # append line to correct dataset split
+            mocked_data[name].append(dataset_line)
+            f.write(
+                f'{rand_string_1}"\t"{rand_string_2}\t{rand_string_3}\t{i}\t{label}\t{rand_string_4}\t{rand_string_5}\n'
+            )
+
+    compressed_dataset_path = os.path.join(base_dir, "Stsbenchmark.tar.gz")
+    # create tar file from dataset folder
+    with tarfile.open(compressed_dataset_path, "w:gz") as tar:
+        tar.add(temp_dataset_dir, arcname="stsbenchmark")
+
+    return mocked_data
+
+
+class TestSTSB(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", "dev", "test"])
+    def test_stsb(self, split):
+        dataset = STSB(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", "dev", "test"])
+    def test_stsb_split_argument(self, split):
+        dataset1 = STSB(root=self.root_dir, split=split)
+        (dataset2,) = STSB(root=self.root_dir, split=(split,))
+
+        for d1, d2 in zip_equal(dataset1, dataset2):
+            self.assertEqual(d1, d2)

torchtext/datasets/__init__.py

@@ -16,6 +16,7 @@
 from .squad1 import SQuAD1
 from .squad2 import SQuAD2
 from .sst2 import SST2
+from .stsb import STSB
 from .udpos import UDPOS
 from .wikitext103 import WikiText103
 from .wikitext2 import WikiText2
@@ -40,6 +41,7 @@
     "SQuAD2": SQuAD2,
     "SogouNews": SogouNews,
     "SST2": SST2,
+    "STSB": STSB,
     "UDPOS": UDPOS,
     "WikiText103": WikiText103,
     "WikiText2": WikiText2,

torchtext/datasets/stsb.py

@@ -0,0 +1,90 @@
+import csv
+import os
+
+from torchtext._internal.module_utils import is_module_available
+from torchtext.data.datasets_utils import (
+    _create_dataset_directory,
+    _wrap_split_argument,
+)
+
+if is_module_available("torchdata"):
+    from torchdata.datapipes.iter import FileOpener, IterableWrapper
+
+    # 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 = "http://ixa2.si.ehu.es/stswiki/images/4/48/Stsbenchmark.tar.gz"
+
+MD5 = "4eb0065aba063ef77873d3a9c8088811"
+
+NUM_LINES = {
+    "train": 5749,
+    "dev": 1500,
+    "test": 1379,
+}
+
+_PATH = "Stsbenchmark.tar.gz"
+
+DATASET_NAME = "STSB"
+
+_EXTRACTED_FILES = {
+    "train": os.path.join("stsbenchmark", "sts-train.csv"),
+    "dev": os.path.join("stsbenchmark", "sts-dev.csv"),
+    "test": os.path.join("stsbenchmark", "sts-test.csv"),
+}
+
+
+@_create_dataset_directory(dataset_name=DATASET_NAME)
+@_wrap_split_argument(("train", "dev", "test"))
+def STSB(root, split):
+    """STSB Dataset
+
+    For additional details refer to https://ixa2.si.ehu.eus/stswiki/index.php/STSbenchmark
+
+    Number of lines per split:
+        - train: 5749
+        - dev: 1500
+        - test: 1379
+
+    Args:
+        root: Directory where the datasets are saved. Default: os.path.expanduser('~/.torchtext/cache')
+        split: split or splits to be returned. Can be a string or tuple of strings. Default: (`train`, `dev`, `test`)
+
+    :returns: DataPipe that yields tuple of (index (int), label (float), sentence1 (str), sentence2 (str))
+    :rtype: (int, float, 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`"
+        )
+
+    def _filepath_fn(x=_PATH):
+        return os.path.join(root, os.path.basename(x))
+
+    def _extracted_filepath_fn(_=None):
+        return _filepath_fn(_EXTRACTED_FILES[split])
+
+    def _filter_fn(x):
+        return _EXTRACTED_FILES[split] in x[0]
+
+    def _modify_res(x):
+        return (int(x[3]), float(x[4]), x[5], x[6])
+
+    url_dp = IterableWrapper([URL])
+    cache_compressed_dp = url_dp.on_disk_cache(
+        filepath_fn=_filepath_fn,
+        hash_dict={_filepath_fn(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=_extracted_filepath_fn)
+    cache_decompressed_dp = FileOpener(cache_decompressed_dp, mode="b").read_from_tar().filter(_filter_fn)
+    cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
+
+    data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8")
+    parsed_data = data_dp.parse_csv(delimiter="\t", quoting=csv.QUOTE_NONE).map(_modify_res)
+    return parsed_data.shuffle().set_shuffle(False).sharding_filter()