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Source code for torchtext.datasets.stsb

import csv
import os
from functools import partial

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
from torchtext._internal.module_utils import is_module_available
from torchtext.data.datasets_utils import (
    _create_dataset_directory,
    _wrap_split_argument,
)


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"),
}


def _filepath_fn(root, x=_PATH):
    return os.path.join(root, os.path.basename(x))


def _extracted_filepath_fn(root, split, _=None):
    return _filepath_fn(root, _EXTRACTED_FILES[split])


def _filter_fn(split, x):
    return _EXTRACTED_FILES[split] in x[0]


def _modify_res(x):
    return (int(x[3]), float(x[4]), x[5], x[6])


[docs]@_create_dataset_directory(dataset_name=DATASET_NAME) @_wrap_split_argument(("train", "dev", "test")) def STSB(root, split): """STSB 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://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" ) 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_tar().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(delimiter="\t", quoting=csv.QUOTE_NONE).map(_modify_res) return parsed_data.shuffle().set_shuffle(False).sharding_filter()

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