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# Copyright (c) Facebook, Inc. and its affiliates.
import os
from functools import partial
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 = "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"),
}
def _filepath_fn(root, _=None):
return os.path.join(root, os.path.basename(URL))
def _extracted_filepath_fn(root, split, _=None):
return os.path.join(root, _EXTRACTED_FILES[split])
def _filter_fn(split, x):
return _EXTRACTED_FILES[split] in x[0]
def _modify_test_res(t):
return (t[1].strip(),)
def _modify_res(t):
return t[0].strip(), int(t[1])
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "dev", "test"))
def SST2(root, split):
"""SST2 Dataset
.. warning::
using datapipes is still currently subject to a few caveats. if you wish
to use this dataset with shuffling, multi-processing, or distributed
learning, please see :ref:`this note <datapipes_warnings>` for further
instructions.
For additional details refer to https://nlp.stanford.edu/sentiment/
Number of lines per split:
- train: 67349
- dev: 872
- test: 1821
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 text and/or label (1 to 4). The `test` split only returns text.
:rtype: Union[(int, 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"
)
url_dp = IterableWrapper([URL])
cache_compressed_dp = url_dp.on_disk_cache(
filepath_fn=partial(_filepath_fn, root),
hash_dict={_filepath_fn(root): 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=partial(_extracted_filepath_fn, root, split))
cache_decompressed_dp = (
FileOpener(cache_decompressed_dp, mode="b").load_from_zip().filter(partial(_filter_fn, split))
)
cache_decompressed_dp = cache_decompressed_dp.end_caching(mode="wb", same_filepath_fn=True)
data_dp = FileOpener(cache_decompressed_dp, encoding="utf-8")
# test split for SST2 doesn't have labels
if split == "test":
parsed_data = data_dp.parse_csv(skip_lines=1, delimiter="\t").map(_modify_test_res)
else:
parsed_data = data_dp.parse_csv(skip_lines=1, delimiter="\t").map(_modify_res)
return parsed_data.shuffle().set_shuffle(False).sharding_filter()
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