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# Copyright (c) Facebook, Inc. and its affiliates.
import csv
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://cims.nyu.edu/~sbowman/multinli/multinli_1.0.zip"
MD5 = "0f70aaf66293b3c088a864891db51353"
NUM_LINES = {
"train": 392702,
"dev_matched": 9815,
"dev_mismatched": 9832,
}
_PATH = "multinli_1.0.zip"
DATASET_NAME = "MNLI"
_EXTRACTED_FILES = {
"train": "multinli_1.0_train.txt",
"dev_matched": "multinli_1.0_dev_matched.txt",
"dev_mismatched": "multinli_1.0_dev_mismatched.txt",
}
LABEL_TO_INT = {"entailment": 0, "neutral": 1, "contradiction": 2}
def _filepath_fn(root, x=None):
return os.path.join(root, os.path.basename(x))
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 _filter_res(x):
return x[0] in LABEL_TO_INT
def _modify_res(x):
return (LABEL_TO_INT[x[0]], x[5], x[6])
@_create_dataset_directory(dataset_name=DATASET_NAME)
@_wrap_split_argument(("train", "dev_matched", "dev_mismatched"))
def MNLI(root, split):
"""MNLI 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://cims.nyu.edu/~sbowman/multinli/
Number of lines per split:
- train: 392702
- dev_matched: 9815
- dev_mismatched: 9832
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_matched`, `dev_mismatched`)
:returns: DataPipe that yields tuple of text and label (0 to 2).
:rtype: Tuple[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, 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=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")
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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