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import csv
import os
from functools import partial
from typing import Union, Tuple
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
from torchtext._download_hooks import HttpReader
URL = "https://nyu-mll.github.io/CoLA/cola_public_1.1.zip"
MD5 = "9f6d88c3558ec424cd9d66ea03589aba"
_PATH = "cola_public_1.1.zip"
NUM_LINES = {"train": 8551, "dev": 527, "test": 516}
_EXTRACTED_FILES = {
"train": os.path.join("cola_public", "raw", "in_domain_train.tsv"),
"dev": os.path.join("cola_public", "raw", "in_domain_dev.tsv"),
"test": os.path.join("cola_public", "raw", "out_of_domain_dev.tsv"),
}
DATASET_NAME = "CoLA"
def _filepath_fn(root, _=None):
return os.path.join(root, _PATH)
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_res(t):
return (t[0], int(t[1]), t[3])
def _filter_res(x):
return len(x) == 4
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "dev", "test"))
def CoLA(root: str, split: Union[Tuple[str], str]):
"""CoLA 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://nyu-mll.github.io/CoLA/
Number of lines per split:
- train: 8551
- dev: 527
- test: 516
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 rows from CoLA dataset (source (str), label (int), sentence (str))
:rtype: (str, int, str)
"""
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")
# some context stored at top of the file needs to be removed
parsed_data = (
data_dp.parse_csv(skip_lines=1, delimiter="\t", quoting=csv.QUOTE_NONE).filter(_filter_res).map(_modify_res)
)
return parsed_data.shuffle().set_shuffle(False).sharding_filter()
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