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Source code for torchtext.datasets.mnli

# 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])


[docs]@_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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